Digitalisation: how to save time and cost during the design and development cycle

Digitalisation: how to save time and cost during the design and development cycle

In high value industries, ‘digital twins’ are an increasingly potent talking point. Ian Risk, CTO at CFMS, explains how bridging the gap between digital simulation models and physical assets reduces costs, risk and valuable time-to-market.

RobotarmThe arrival of the digital era has resulted in a shift in focus for engineering enterprises from increasing scales of production, to how to minimise the productivity gap and improve responsiveness to both market and consumer demands. For example, aerospace manufacturers are attempting to reduce the product development cycle from nine years to five. Similarly, within the automotive industry questions are being asked as to how to eliminate aspects of the development cycle, or at the very least enhance them to improve efficiency. Digitalisation has the potential to realise these goals, and in recent years several factors, such as the availability and cost of computational power, have converged to create an environment in which digital technologies can thrive.   

The digital transformation taking place within high value industries is nothing new. In many industries, developments in a digital model-based approach to systems engineering and the means of embedding greater amounts of information and detail into those digital models, have been occurring for many years. Previously, these conversations centred on understanding how to create better designs. Today, they are much more about creating the through-life digital architecture by which those designs can be realised faster and at less cost. A prime example is the use of a purely digital model-based approach at the start of the development cycle to challenge and validate requirements far more rigorously. By doing this, significant amounts of time and cost are saved prior to undertaking a detailed design. Iterative loops are reduced, and the areas requiring detailed analysis can be carefully targeted, ultimately minimising the need for physical testing.

The challenge

For complex industrial or engineered products, achieving or implementing these digital processes to reap the benefits of digitalisation is extremely challenging. Taking such a step requires careful exploration of key, connected processes. At the same time, enterprises and their operations have to continue with minimum disruption whilst they transform both business and engineering capability. Then there is the question of investment.

Any high value enterprise will have invested a considerable amount of time and energy in building in-house expertise and implicit knowledge. That knowledge will, for example, often be found within a chief engineer. When a customer makes a request – an aircraft of a certain size, able to carry a specific number of passengers, over a particular distance, for example – the chief engineer will formulate the basis of a design in their head. It might not be the optimum design, but it will be a design they know well and therefore trust.

Through digitalisation we can challenge that process by analysing the product throughout its lifetime, and then applying artificial intelligence techniques to formulate alternative solutions. This level of automation can result in some cultural resistance around whether the value of experienced engineers is being diminished. In reality, it is an opportunity for engineers to be more creative, and to consider options they might not have envisioned at the start of the design process. Moreover, the life experience of any given engineer is only such that they will inevitably focus on certain potential solutions, and will never have the time or ability to explore alternatives within the full design space. These restrictions are an instant barrier to innovation.  

A neutral view

At the Centre for Modelling & Simulation (CFMS) we embrace the concept of generative design. As an independent, not-for-profit, trusted digital test bed for the design of high value engineering products and processes, we provide the opportunity and foundation for through-life engineering. We do this by creating a virtual replica of the systems and processes used to investigate options in advance of physical development. Through efficient exploration of the design space and digital arena, engineers can arrive at potential solutions that meet requirements and offer an initial sense check in terms of quality, cost, and performance. By reducing the level of uncertainty at the design stage, the need for detailed simulation and analyses is likewise reduced.

For those taking their first step into this space, the important thing is not to be afraid to explore the options associated with this type of technology. Digitalisation is changing the future of product design, and new technologies like additive manufacturing (also known as 3D printing) continue to emerge, offering far more flexibility and freedom. During their use, products become the largest data source, and by capturing that knowledge and feeding it back into digital models, engineering teams can gain valuable insight and mature their designs.  

Starting at model-based engineering, CFMS supports every aspect of that design and development journey. The Centre has advanced simulation and data science capabilities that can be applied enterprise-wide, at an individual process level, or in a specific phase of the lifecycle – within a particular manufacturing operation, for example. CFMS can demonstrate that the concept of the digital twin goes far beyond having a CAD model of a product – it's an understanding of how that product has been designed, and how it will operate throughout its lifecycle. By recognising and embracing the concept and benefits of having a complete digital twin of their product, enterprises will reduce cost and time-to-market, whilst increasing their competitive edge.

To arrange a demonstration or for more information contact CFMS.

 

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